由于超参数调优似乎是为了同一个任务训练不同的模型，我想并行训练它们是一个好主意，以获得一些时间。然而，我的尝试非常不成功，因为在执行代码时出现了多个错误。我想知道使用keras是否需要我以不同的方式编写多线程，或者问题是否在其他地方。
下面是我写的（我试图计算dropout对自定义度量的最小值的影响）：

from threading import Thread

class FitModel(Thread):

    def __init__(self, params):
        Thread.__init__(self)
        self.params = params

    def run(self):
        DC=DeltaCallback(verbose=0)  #custom metric
    
        model=keras.models.Sequential([
            keras.layers.Conv1D(64,11,activation="relu",padding="SAME",input_shape=(700,1)),
            keras.layers.BatchNormalization(),
            keras.layers.AvgPool1D(pool_size=2),
            keras.layers.Conv1D(128,11,activation="relu",padding="SAME"),
            keras.layers.BatchNormalization(),
            keras.layers.AvgPool1D(pool_size=2),
            keras.layers.Conv1D(256,11,activation="relu",padding="SAME"),
            keras.layers.BatchNormalization(),
            keras.layers.AvgPool1D(pool_size=2),
            keras.layers.Conv1D(512,11,activation="relu",padding="SAME"),
            keras.layers.BatchNormalization(),
            keras.layers.AvgPool1D(pool_size=2),
            keras.layers.Conv1D(512,11,activation="relu",padding="SAME"),
            keras.layers.BatchNormalization(),
            keras.layers.AvgPool1D(pool_size=2),
            keras.layers.Flatten(),
            keras.layers.Dropout(self.params[0]),
            keras.layers.Dense(4096,activation="relu"),
            keras.layers.Dropout(self.params[1]),
            keras.layers.Dense(4096,activation="relu"),
            keras.layers.Dense(256,activation="softmax")
        ])
        model.compile(loss="sparse_categorical_crossentropy",
             optimizer=keras.optimizers.RMSprop(learning_rate=0.00001),
             metrics=["accuracy"])

        model.fit(X_train,y_train,batch_size=100,epochs=50,validation_data=(X_valid,y_valid),callbacks=[DC],verbose=2)

        print(self.params,"epochs : ",DC.deltas.index(min(DC.deltas)))
        print(self.params,"deltamin : ",DC.deltas[DC.deltas.index(min(DC.deltas))])
        print(self.params,"Nval : ", DC.Nvals[DC.deltas.index(min(DC.deltas))])

        
parameters_list=[[0,0.1],[0.1,0],[0.2,0],[0.1,0.2],[0.2,0.1],[0.3,0],[0.3,0.1],[0.3,0.2],[0,0.3],[0.1,0.3],[0.2,0.3]]
    
# create threads
THREADS = [FitModel(parameters) for parameters in parameters_list]
    
# start threads
for thread in THREADS:
    thread.start()

# wait for threads to finish
for thread in THREADS:
    thread.join()

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问题是，当我尝试执行这段代码时，会出现多个错误，以及OOM错误。